Seq2Seq dialogs predicts only most common words like `you` after couple of epoches
To answer my own question - it was because of 2 things: 


Too small number of batches - Model just started to gain statistical knowledge about language dialogs. I needed to train it longer.
I masked sequence too early (wrongly removed the &lt; END > tag) - Because on each sentence the last world is just &lt; END > tag - I removed it on all training examples, which prevented Model from learn what does it mean "the end of the sentence".


The last condition probably caused that strange pattern even further, because if model doesn't know what word to put in (and because of the lack of the &lt; END > tag) it must fill each sentence till the end of max_sequence_len. 

So the Model inputed in loop-like-manner - one of the most common word (where there was no signal from target sentence, because it simply ended).